Updated: 2020-08-23 06:37:29 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from \(log_2\)(\(R_e\)) > 0 to \(log_2\)(\(R_e\)) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

State Level Data


County Level Data


state R_e cases daily_cases
Massachusetts 1.54 123960 410
Maine 1.27 4315 28
North Dakota 1.27 9577 202
South Dakota 1.27 10820 146
Wyoming 1.22 3553 51
Iowa 1.21 56113 701
Mississippi 1.12 77066 866
North Carolina 1.11 153590 1531
Illinois 1.10 218811 2080
Oklahoma 1.10 52348 748
Kentucky 1.07 45248 729
Montana 1.07 6293 115
Kansas 1.06 37634 510
Missouri 1.06 66553 1122
Utah 1.06 48883 390
Minnesota 1.05 68616 657
Arkansas 1.04 55368 629
Rhode Island 1.04 19207 100
Nebraska 1.03 31695 262
Wisconsin 1.03 70120 754
Tennessee 1.02 139464 1598
Indiana 1.01 87233 884
West Virginia 1.01 9170 119
Alabama 1.00 114525 1023
Connecticut 0.99 51363 87
Georgia 0.99 233150 2716
New York 0.99 433858 633
Ohio 0.99 114100 1004
South Carolina 0.99 111134 818
Oregon 0.98 24716 261
Virginia 0.98 88756 698
Colorado 0.97 54904 305
Nevada 0.97 65227 673
Washington 0.97 73448 608
New Mexico 0.96 24176 137
Pennsylvania 0.96 133177 705
Michigan 0.95 105508 640
Texas 0.95 601454 6574
Maryland 0.93 104067 571
Arizona 0.92 197891 751
New Jersey 0.91 190800 334
Idaho 0.90 29894 343
California 0.89 668085 6829
Vermont 0.88 1538 6
Florida 0.87 597578 4370
New Hampshire 0.87 7091 17
Louisiana 0.83 141939 698
Delaware 0.78 16616 75

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

Mortality Trend

\(R_e\) Trend

National effective reproduction rate

Distribution of \(R_e\) Values

Howver, there is a wiude dirstubtion of \(R_e\) across regions and counties. The distributions in the graph below looks roughly symmetrical because the x-scale is logarithmic.

Distribution of Baseline Control

Similarly for disease control, when take at the county level, there is a wide distribution of Baseline Control.

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
Whitman WA 27 1 1.8 154 320 5
Grays Harbor WA 25 2 1.6 178 250 8
King WA 1 3 1.0 18614 860 157
Kitsap WA 16 4 1.3 906 350 18
Grant WA 9 5 1.1 2126 2240 48
Pierce WA 3 6 1.0 7129 830 68
Snohomish WA 4 7 0.9 6780 860 42
Benton WA 6 9 1.1 4143 2130 21
Yakima WA 2 12 0.9 11465 4600 37
Clark WA 8 13 0.9 2409 520 24
Franklin WA 7 17 0.9 3968 4380 22
Spokane WA 5 19 0.7 4994 1000 34
OR
county ST case rank severity R_e cases cases/100k daily cases
Marion OR 3 1 1.1 3471 1030 49
Jackson OR 7 2 1.2 673 310 20
Multnomah OR 1 3 0.9 5607 700 49
Washington OR 2 4 1.0 3514 600 32
Malheur OR 6 5 1.0 1008 3310 18
Clackamas OR 5 6 1.0 1792 440 20
Yamhill OR 10 7 1.1 576 550 11
Umatilla OR 4 10 0.8 2542 3310 16
Lane OR 9 16 0.9 652 180 4
Deschutes OR 8 19 0.8 664 370 4
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Los Angeles CA 1 1 0.9 230745 2290 1529
Fresno CA 7 2 1.1 22987 2350 468
San Bernardino CA 4 3 1.0 44791 2100 656
Alameda CA 8 4 1.0 16763 1020 289
Sonoma CA 25 5 1.1 5081 1010 127
Orange CA 3 6 0.9 45810 1450 380
Riverside CA 2 7 0.8 50064 2100 512
San Diego CA 5 8 0.9 36270 1100 263
Kern CA 6 14 0.7 27998 3170 242
San Joaquin CA 9 22 0.7 16027 2190 164

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Maricopa AZ 1 1 0.9 131636 3090 402
Pinal AZ 4 2 1.2 9166 2180 69
Pima AZ 2 3 0.8 20486 2010 153
Graham AZ 13 4 1.2 656 1730 10
Gila AZ 12 5 1.1 1066 2000 12
Mohave AZ 6 6 1.0 3484 1690 20
Yuma AZ 3 7 0.9 12047 5800 31
Apache AZ 7 8 1.0 3295 4610 9
Coconino AZ 8 10 0.9 3234 2310 10
Navajo AZ 5 11 0.8 5498 5060 8
Santa Cruz AZ 9 12 0.9 2730 5860 4
CO
county ST case rank severity R_e cases cases/100k daily cases
Adams CO 3 1 1.1 7125 1430 53
Denver CO 1 2 1.0 10836 1560 46
El Paso CO 4 3 1.0 5786 840 47
Arapahoe CO 2 4 1.0 7790 1220 36
Jefferson CO 5 5 0.9 4563 800 26
Larimer CO 9 6 1.0 1785 530 18
Boulder CO 7 7 1.1 2209 690 11
Weld CO 6 8 1.0 3916 1330 15
Douglas CO 8 10 1.0 1899 580 12
UT
county ST case rank severity R_e cases cases/100k daily cases
Utah UT 2 1 1.1 10032 1700 111
Salt Lake UT 1 2 1.0 22706 2030 156
Sanpete UT 13 3 1.7 158 540 4
Summit UT 7 4 1.5 801 1980 11
Juab UT 15 5 1.6 102 930 4
Millard UT 14 6 1.7 147 1150 2
Davis UT 3 7 1.1 3571 1050 30
Cache UT 6 8 1.2 2028 1660 10
Washington UT 5 9 1.1 2704 1680 17
Weber UT 4 10 1.0 3079 1240 23
Tooele UT 9 13 0.9 641 980 4
San Juan UT 8 15 1.1 661 4330 1
NM
county ST case rank severity R_e cases cases/100k daily cases
Bernalillo NM 1 1 1.0 5524 820 31
Rio Arriba NM 14 2 1.4 342 870 3
Roosevelt NM 16 3 1.4 190 990 3
Santa Fe NM 8 4 1.2 754 510 10
Chaves NM 11 5 1.0 606 930 12
Lea NM 7 6 1.0 1027 1460 18
Curry NM 10 7 1.0 631 1260 6
Cibola NM 9 9 1.1 720 2670 3
Sandoval NM 5 10 1.0 1181 840 4
San Juan NM 3 11 0.9 3131 2460 6
Doña Ana NM 4 12 0.6 2718 1260 12
McKinley NM 2 14 0.8 4149 5700 6
Otero NM 6 16 0.9 1113 1690 1

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Hunterdon NJ 19 1 1.5 1240 990 5
Ocean NJ 7 2 1.1 10961 1850 25
Passaic NJ 5 3 0.9 18286 3630 35
Middlesex NJ 4 4 1.0 18450 2230 26
Essex NJ 2 5 1.0 20325 2560 26
Burlington NJ 12 6 1.0 6302 1410 20
Gloucester NJ 15 7 1.0 3552 1220 22
Camden NJ 9 10 0.9 9006 1780 26
Hudson NJ 3 12 0.8 20189 3020 23
Union NJ 6 14 0.8 17135 3100 17
Monmouth NJ 8 15 0.8 10669 1710 16
Bergen NJ 1 17 0.7 21607 2320 28
PA
county ST case rank severity R_e cases cases/100k daily cases
Susquehanna PA 41 1 1.6 242 590 4
Philadelphia PA 1 2 0.9 32926 2090 117
Montgomery PA 2 3 1.1 10658 1300 45
Allegheny PA 4 4 0.9 9822 800 68
Pike PA 28 5 1.8 532 960 1
Berks PA 7 6 1.1 5775 1390 33
Delaware PA 3 7 0.9 10041 1780 55
Lancaster PA 6 9 1.0 6420 1190 38
Bucks PA 5 12 0.9 7566 1210 27
Chester PA 8 22 0.9 5459 1060 22
Lehigh PA 9 40 0.7 5123 1410 9
MD
county ST case rank severity R_e cases cases/100k daily cases
Baltimore MD 3 1 1.0 14585 1760 110
Prince George’s MD 1 2 0.9 25754 2840 111
Somerset MD 23 3 1.5 173 670 4
Anne Arundel MD 5 4 1.0 7919 1390 43
Montgomery MD 2 5 0.9 19442 1870 68
Baltimore city MD 4 6 0.8 13927 2270 83
Harford MD 8 7 1.0 2285 910 25
Frederick MD 7 9 1.0 3334 1340 20
Howard MD 6 10 0.9 4207 1330 23
Charles MD 9 12 0.9 2258 1430 15
VA
county ST case rank severity R_e cases cases/100k daily cases
Tazewell VA 68 1 1.7 157 370 5
Fairfax VA 1 2 1.0 17517 1530 92
Smyth VA 54 3 1.5 208 670 7
Prince William VA 2 4 1.0 10251 2240 65
Appomattox VA 72 5 1.4 129 830 5
Lunenburg VA 84 6 1.6 79 640 2
Montgomery VA 37 7 1.3 357 360 6
Arlington VA 8 10 1.1 3358 1450 24
Virginia Beach city VA 4 12 0.9 5673 1260 48
Chesterfield VA 5 13 1.0 4814 1420 33
Newport News city VA 9 14 1.0 2102 1170 22
Loudoun VA 3 16 1.0 5679 1470 30
Henrico VA 6 17 1.0 4296 1320 31
Norfolk city VA 7 20 0.9 4161 1690 32
WV
county ST case rank severity R_e cases cases/100k daily cases
Jackson WV 18 1 1.8 183 630 3
Marshall WV 19 2 2.0 134 420 1
Taylor WV 27 3 1.4 97 570 5
Monongalia WV 2 4 1.2 1035 980 9
Logan WV 5 5 1.1 446 1320 19
Kanawha WV 1 6 1.0 1198 650 21
Mercer WV 10 7 1.1 270 450 7
Jefferson WV 7 8 1.3 317 560 2
Cabell WV 4 11 0.9 494 520 6
Berkeley WV 3 12 0.9 764 670 5
Raleigh WV 6 16 0.7 325 430 4
Wood WV 8 20 0.7 294 340 2
Ohio WV 9 22 0.8 287 670 1
DE
county ST case rank severity R_e cases cases/100k daily cases
New Castle DE 1 1 1.0 7785 1400 43
Kent DE 3 2 0.7 2556 1460 14
Sussex DE 2 3 0.6 6276 2860 19

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Cleburne AL 66 1 1.7 181 1210 7
Houston AL 16 2 1.4 1720 1650 29
Tuscaloosa AL 5 3 1.2 4958 2400 61
Geneva AL 59 4 1.6 352 1330 9
Calhoun AL 12 5 1.2 2275 1980 40
Etowah AL 11 6 1.2 2571 2500 36
Marshall AL 8 7 1.2 3494 3670 25
Jefferson AL 1 8 0.9 14999 2270 115
Lee AL 9 11 1.1 3249 2040 35
Mobile AL 2 20 0.8 11637 2810 83
Montgomery AL 3 24 0.9 7572 3340 48
Shelby AL 7 28 0.9 3912 1850 30
Madison AL 4 30 0.9 6037 1690 36
Baldwin AL 6 38 0.8 4110 1970 30
MS
county ST case rank severity R_e cases cases/100k daily cases
Leflore MS 19 1 1.6 1189 3990 34
Madison MS 4 2 1.4 2738 2650 33
DeSoto MS 2 3 1.2 4228 2400 55
Rankin MS 6 4 1.3 2601 1720 33
Jackson MS 5 5 1.3 2715 1910 42
Lauderdale MS 11 6 1.3 1605 2080 20
Hinds MS 1 7 1.1 6200 2560 50
Lee MS 9 9 1.1 1961 2310 42
Harrison MS 3 13 1.0 3008 1480 40
Forrest MS 8 38 0.9 1995 2640 14
Jones MS 7 43 0.9 2055 3000 11
LA
county ST case rank severity R_e cases cases/100k daily cases
East Feliciana LA 43 1 1.4 704 3610 13
Rapides LA 10 2 1.1 3644 2770 31
Calcasieu LA 5 3 1.1 7248 3620 34
East Baton Rouge LA 2 4 0.9 13266 2990 73
Vernon LA 37 5 1.2 870 1710 10
West Feliciana LA 51 6 1.2 472 3070 11
Caddo LA 6 7 0.9 7124 2870 32
Tangipahoa LA 9 8 0.9 3923 3010 33
Ouachita LA 8 9 0.9 5326 3410 33
St. Tammany LA 7 10 0.9 5786 2300 38
Jefferson LA 1 12 0.7 16043 3690 42
Orleans LA 3 15 0.8 11154 2860 26
Lafayette LA 4 22 0.6 8122 3380 24

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Miami-Dade FL 1 1 0.8 151719 5590 1115
Broward FL 2 2 0.9 69013 3610 443
Hillsborough FL 4 3 1.0 35546 2580 229
Lafayette FL 46 4 1.1 1416 16190 107
Orange FL 5 5 0.9 34562 2620 208
Palm Beach FL 3 6 0.9 40402 2790 223
Marion FL 15 7 1.0 7929 2280 114
Pinellas FL 7 8 1.0 19342 2020 110
Duval FL 6 9 0.9 25447 2750 141
Polk FL 9 11 0.9 16159 2420 128
Lee FL 8 14 0.9 17938 2500 100
GA
county ST case rank severity R_e cases cases/100k daily cases
Stewart GA 108 1 1.8 311 5150 8
Lumpkin GA 75 2 1.5 540 1690 24
Fulton GA 1 3 1.1 23597 2310 270
Chattahoochee GA 51 4 1.6 903 8390 18
Gwinnett GA 2 5 1.0 22975 2550 245
Clinch GA 128 6 1.6 242 3590 6
Clayton GA 7 7 1.1 5978 2150 81
DeKalb GA 3 8 0.9 15878 2140 142
Cobb GA 4 10 0.9 15803 2120 154
Hall GA 5 11 1.1 6983 3560 74
Richmond GA 8 12 1.0 5649 2800 99
Chatham GA 6 25 0.9 6635 2310 63
Muscogee GA 9 48 0.8 5273 2680 34

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Dallas TX 2 1 1.0 72055 2790 1276
Montgomery TX 17 2 1.4 7942 1430 151
Live Oak TX 122 3 2.0 265 2190 5
Harris TX 1 4 1.0 97595 2120 912
Hidalgo TX 6 5 1.1 24122 2840 363
Travis TX 5 6 1.1 25732 2140 247
Crockett TX 158 7 2.3 159 4380 0
Tarrant TX 4 12 0.8 39913 1980 410
Cameron TX 8 13 1.0 19075 4520 170
Bexar TX 3 15 1.0 44966 2330 164
El Paso TX 7 20 0.8 19487 2330 202
Nueces TX 9 43 0.7 18010 5000 156
OK
county ST case rank severity R_e cases cases/100k daily cases
Haskell OK 45 1 1.8 142 1120 12
Cleveland OK 3 2 1.3 3488 1260 51
Comanche OK 8 3 1.5 978 800 19
Oklahoma OK 1 4 1.1 12447 1590 156
Tulsa OK 2 5 1.1 12208 1900 143
Kingfisher OK 42 6 1.5 199 1270 8
Payne OK 10 7 1.3 882 1080 15
McCurtain OK 9 9 1.4 936 2840 8
Wagoner OK 7 18 1.1 1044 1340 15
Canadian OK 4 21 1.0 1403 1030 14
Rogers OK 5 31 0.8 1210 1330 15
Texas OK 6 37 1.0 1094 5180 3

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Isabella MI 36 1 1.9 267 380 9
Wayne MI 1 2 0.9 29870 1700 121
Wexford MI 53 3 1.7 85 260 2
Oakland MI 2 4 0.9 17092 1370 108
Macomb MI 3 5 0.9 12110 1390 94
Monroe MI 16 6 1.2 1161 780 16
Saginaw MI 8 7 1.1 2318 1200 25
Kent MI 4 9 1.0 7975 1240 33
Genesee MI 5 12 1.1 3837 940 16
Washtenaw MI 6 16 1.0 3257 890 14
Ottawa MI 9 26 0.8 1995 700 10
Jackson MI 7 28 1.0 2488 1570 4
WI
county ST case rank severity R_e cases cases/100k daily cases
Brown WI 4 1 1.3 4828 1860 59
Milwaukee WI 1 2 1.0 23158 2430 166
Iron WI 50 3 1.7 104 1820 4
Oconto WI 31 4 1.4 350 930 12
Clark WI 39 5 1.6 216 630 4
Washington WI 10 6 1.1 1456 1080 35
Waukesha WI 2 7 1.0 5223 1310 75
Outagamie WI 9 10 1.1 1531 830 25
Dane WI 3 11 0.9 5056 950 39
Rock WI 7 12 1.2 1679 1040 11
Racine WI 5 13 1.0 3818 1950 24
Walworth WI 8 18 0.9 1595 1550 19
Kenosha WI 6 20 1.0 2873 1710 15

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
Le Sueur MN 26 1 1.6 305 1090 11
Hennepin MN 1 2 1.0 21435 1730 175
Stearns MN 5 3 1.4 3055 1950 18
Dakota MN 3 4 1.1 5185 1240 65
Ramsey MN 2 5 1.0 8477 1570 74
Chisago MN 31 6 1.5 251 460 6
Washington MN 6 7 1.1 2560 1010 39
Anoka MN 4 10 1.0 4283 1230 48
Scott MN 8 21 0.9 1824 1270 19
Olmsted MN 7 28 0.9 1905 1240 12
Nobles MN 9 30 1.1 1819 8330 5
SD
county ST case rank severity R_e cases cases/100k daily cases
Meade SD 11 1 1.9 144 530 8
Minnehaha SD 1 2 1.2 4854 2600 41
Pennington SD 2 3 1.4 1009 920 13
Brown SD 5 4 1.4 528 1360 10
Codington SD 7 5 1.4 204 730 8
Lawrence SD 17 6 1.4 95 380 5
Lincoln SD 3 7 1.2 778 1420 13
Beadle SD 4 8 1.5 607 3300 2
Brookings SD 9 9 1.2 173 510 4
Yankton SD 8 10 1.0 175 770 5
Union SD 6 11 1.2 231 1520 2
ND
county ST case rank severity R_e cases cases/100k daily cases
Ward ND 6 1 1.6 372 540 20
Grand Forks ND 3 2 1.5 936 1330 32
Burleigh ND 2 3 1.3 1656 1770 50
Stark ND 5 4 1.3 512 1660 25
Cass ND 1 5 1.2 3239 1860 20
Walsh ND 10 6 1.5 138 1280 4
Stutsman ND 11 7 1.6 136 650 1
Benson ND 8 8 1.3 204 2960 7
Morton ND 4 9 1.1 527 1730 13
Williams ND 7 12 0.9 324 950 4
Mountrail ND 9 17 0.6 161 1590 1

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
Tolland CT 7 1 1.6 1085 720 4
Fairfield CT 1 2 1.1 18458 1950 38
Hartford CT 3 3 1.0 13012 1450 18
New Haven CT 2 4 0.9 13454 1570 19
Litchfield CT 4 5 0.9 1646 900 2
New London CT 5 6 0.7 1509 560 4
Middlesex CT 6 7 0.7 1428 870 2
Windham CT 8 8 0.7 770 660 2
MA
county ST case rank severity R_e cases cases/100k daily cases
Suffolk MA 2 1 1.6 22690 2870 108
Essex MA 3 2 1.6 18414 2360 77
Worcester MA 4 3 1.6 13941 1700 42
Middlesex MA 1 4 1.5 27036 1690 75
Franklin MA 12 5 2.2 418 590 1
Hampden MA 8 6 1.5 7790 1660 24
Plymouth MA 7 7 1.5 9442 1840 24
Norfolk MA 5 8 1.4 10841 1550 25
Bristol MA 6 9 1.4 9559 1710 24
Barnstable MA 9 12 1.3 1816 850 2
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 1.0 16207 2550 84
Washington RI 3 2 1.4 662 520 6
Kent RI 2 3 1.1 1597 970 8
Bristol RI 5 4 1.1 330 670 1
Newport RI 4 5 0.9 412 500 1

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
Essex NY 50 1 2.2 97 260 6
New York City NY 1 2 0.9 236589 2800 289
Erie NY 7 3 1.2 9389 1020 45
Nassau NY 3 4 1.1 44200 3260 47
Rockland NY 5 5 1.3 14117 4360 18
Suffolk NY 2 6 1.0 44453 2990 51
Tioga NY 38 7 1.7 204 420 1
Westchester NY 4 11 0.9 36655 3780 32
Orange NY 6 14 1.1 11340 3000 14
Dutchess NY 9 17 1.1 4748 1620 12
Monroe NY 8 19 0.9 5305 710 23

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Windham VT 3 1 1.4 112 260 1
Chittenden VT 1 2 0.8 772 480 3
Franklin VT 2 3 0.9 122 250 0
Rutland VT 4 4 0.9 103 170 0
Addison VT 6 5 0.8 77 210 0
Bennington VT 5 6 0.6 94 260 0
Windsor VT 7 7 0.4 75 140 0
ME
county ST case rank severity R_e cases cases/100k daily cases
York ME 2 1 1.6 728 360 7
Kennebec ME 5 2 2.0 177 150 1
Penobscot ME 4 3 1.3 215 140 7
Cumberland ME 1 4 1.0 2160 740 6
Androscoggin ME 3 5 1.0 588 550 2
NH
county ST case rank severity R_e cases cases/100k daily cases
Merrimack NH 3 1 1.3 484 320 2
Rockingham NH 2 2 1.0 1753 570 5
Hillsborough NH 1 3 0.8 3983 970 8
Carroll NH 8 4 1.2 99 210 0
Cheshire NH 6 5 0.8 110 140 1
Grafton NH 7 6 0.9 108 120 0
Strafford NH 4 7 0.6 370 290 1
Belknap NH 5 8 0.2 122 200 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Richland SC 3 1 1.0 9848 2410 84
Edgefield SC 42 2 1.4 406 1520 9
Spartanburg SC 6 3 1.1 4676 1550 50
Anderson SC 12 4 1.1 2848 1450 43
Georgetown SC 18 5 1.3 1638 2660 19
Lexington SC 5 6 1.1 5432 1900 39
Marion SC 34 7 1.4 626 1980 8
Charleston SC 1 8 0.9 13249 3360 65
Greenville SC 2 14 0.9 11507 2310 43
Horry SC 4 16 0.9 9078 2830 36
Florence SC 9 17 0.9 3976 2870 39
Beaufort SC 8 21 0.9 4517 2470 28
Berkeley SC 7 26 0.8 4600 2200 26
NC
county ST case rank severity R_e cases cases/100k daily cases
Orange NC 24 1 1.8 1759 1230 60
Cherokee NC 71 2 1.7 356 1290 12
Transylvania NC 80 3 1.7 202 600 9
Wake NC 2 4 1.1 13494 1290 129
Mecklenburg NC 1 5 1.1 24136 2290 159
Scotland NC 58 6 1.4 541 1530 22
Guilford NC 4 7 1.2 6288 1200 61
Union NC 7 9 1.1 3676 1620 50
Gaston NC 6 16 1.1 3783 1750 38
Cumberland NC 8 19 1.0 3676 1110 46
Forsyth NC 5 23 1.0 5774 1550 41
Johnston NC 9 30 1.1 3595 1880 24
Durham NC 3 36 0.9 6618 2160 32

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Rosebud MT 9 1 1.7 133 1440 13
Yellowstone MT 1 2 1.2 1736 1100 43
Flathead MT 4 3 1.2 460 470 12
Big Horn MT 3 4 1.0 587 4390 12
Cascade MT 6 5 1.2 195 240 3
Glacier MT 12 6 1.2 101 740 3
Ravalli MT 13 7 1.3 91 220 1
Lewis and Clark MT 8 8 1.0 191 280 2
Gallatin MT 2 10 0.8 1032 990 5
Missoula MT 5 11 0.7 405 350 4
Lake MT 7 12 1.0 193 650 1
WY
county ST case rank severity R_e cases cases/100k daily cases
Albany WY 10 1 1.8 122 320 5
Sheridan WY 11 2 1.6 114 380 6
Fremont WY 1 3 1.4 571 1420 8
Carbon WY 7 4 1.1 197 1270 9
Natrona WY 6 5 1.2 262 330 3
Laramie WY 2 6 1.1 541 550 4
Teton WY 3 7 1.1 402 1740 2
Sweetwater WY 4 8 1.1 290 660 2
Park WY 9 9 1.1 157 540 2
Uinta WY 5 10 1.2 283 1370 1
Campbell WY 8 12 0.9 160 340 2
ID
county ST case rank severity R_e cases cases/100k daily cases
Payette ID 10 1 1.4 536 2330 16
Power ID 25 2 1.6 100 1300 5
Nez Perce ID 17 3 1.5 229 570 9
Ada ID 1 4 0.9 10575 2370 104
Latah ID 23 5 1.3 182 460 8
Canyon ID 2 6 0.8 6758 3180 61
Bonneville ID 5 7 0.9 1566 1390 34
Bannock ID 6 9 1.0 606 710 13
Jerome ID 9 14 0.9 557 2380 6
Twin Falls ID 4 15 0.8 1593 1900 11
Kootenai ID 3 17 0.7 2075 1350 13
Cassia ID 8 19 0.9 574 2430 3
Blaine ID 7 23 1.0 595 2710 1

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Jackson OH 77 1 1.9 111 340 6
Fayette OH 70 2 1.7 164 570 6
Huron OH 41 3 1.6 453 770 7
Summit OH 6 4 1.2 4049 750 47
Franklin OH 1 5 1.0 20462 1600 153
Henry OH 72 6 1.6 153 560 5
Lucas OH 4 7 1.1 6024 1390 56
Montgomery OH 5 8 1.1 4959 930 50
Cuyahoga OH 2 9 0.9 14884 1190 94
Butler OH 7 10 1.0 3421 900 40
Hamilton OH 3 11 0.9 10505 1290 63
Mahoning OH 9 55 0.8 2754 1190 12
Marion OH 8 76 0.6 2979 4560 2
IL
county ST case rank severity R_e cases cases/100k daily cases
Cook IL 1 1 1.1 120365 2300 765
McLean IL 18 2 1.5 978 560 39
Effingham IL 37 3 1.5 364 1070 26
Cumberland IL 68 4 1.8 99 910 6
Boone IL 22 5 1.7 810 1510 7
DuPage IL 3 6 1.1 13658 1470 128
Fayette IL 65 7 1.7 105 480 5
Will IL 5 8 1.1 10614 1540 118
Winnebago IL 7 11 1.3 4020 1400 26
Kane IL 4 13 1.1 10755 2030 79
St. Clair IL 6 14 1.1 5289 2010 74
Madison IL 9 16 1.0 3520 1320 74
Lake IL 2 18 1.0 13831 1970 87
McHenry IL 8 39 0.9 3610 1170 28
IN
county ST case rank severity R_e cases cases/100k daily cases
Knox IN 53 1 1.7 265 710 15
Greene IN 46 2 1.6 322 1000 9
Daviess IN 41 3 1.5 392 1190 15
Marion IN 1 4 1.0 17478 1850 116
St. Joseph IN 5 5 1.1 4178 1550 57
Lake IN 2 6 1.0 8585 1760 74
Sullivan IN 54 7 1.3 255 1230 13
Hamilton IN 6 10 1.0 3440 1090 48
Allen IN 4 14 0.9 4549 1230 45
Hendricks IN 8 17 1.0 2143 1330 19
Elkhart IN 3 18 0.9 5403 2650 32
Vanderburgh IN 7 22 0.9 2321 1280 24
Johnson IN 9 32 1.0 1945 1280 12

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Blount TN 14 1 1.3 1712 1330 42
Hamilton TN 4 2 1.1 7409 2070 104
Gibson TN 24 3 1.3 964 1960 27
Davidson TN 2 4 0.9 25195 3680 167
Lewis TN 87 5 1.6 110 920 5
Hardin TN 51 6 1.4 586 2270 13
Sullivan TN 20 7 1.2 1346 860 34
Shelby TN 1 8 0.9 26042 2780 171
Knox TN 5 9 1.0 6029 1320 89
Sumner TN 7 12 1.1 3839 2140 36
Williamson TN 6 21 1.0 4104 1880 44
Rutherford TN 3 25 0.9 7428 2420 62
Wilson TN 8 38 0.9 2631 1980 25
Bradley TN 9 40 0.9 2308 2210 27
KY
county ST case rank severity R_e cases cases/100k daily cases
Green KY 72 1 1.8 102 930 8
Jefferson KY 1 2 1.0 11070 1440 221
Oldham KY 11 3 1.4 734 1120 13
Fayette KY 2 4 1.0 4945 1550 86
Marion KY 52 5 1.6 164 850 6
Pulaski KY 20 6 1.4 451 700 11
Warren KY 3 7 1.2 2955 2340 29
Daviess KY 6 10 1.3 906 910 14
Boone KY 5 20 1.1 1214 940 10
Shelby KY 7 30 1.1 863 1840 8
Kenton KY 4 35 0.9 1618 980 13
Hardin KY 8 37 0.9 832 770 13
Christian KY 9 47 0.8 815 1130 11

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
Boone MO 7 1 1.4 1878 1060 54
Greene MO 5 2 1.3 2433 840 89
St. Francois MO 17 3 1.4 796 1200 45
St. Louis MO 1 4 0.9 17801 1780 209
St. Charles MO 3 5 1.1 5190 1330 86
Jackson MO 4 6 1.0 4982 720 71
Miller MO 48 7 1.4 192 770 8
Jefferson MO 6 10 1.0 2358 1060 45
St. Louis city MO 2 16 0.9 5872 1890 45
Clay MO 9 18 1.1 1232 520 16
Jasper MO 8 36 1.0 1437 1210 13
AR
county ST case rank severity R_e cases cases/100k daily cases
Stone AR 56 1 2.0 139 1120 12
Dallas AR 65 2 2.1 83 1120 3
Craighead AR 7 3 1.3 1652 1560 30
Jefferson AR 5 4 1.2 1878 2670 32
Washington AR 1 5 1.2 6588 2880 28
Jackson AR 50 6 1.4 167 970 7
Pope AR 8 7 1.2 1573 2470 24
Pulaski AR 2 8 1.0 6492 1650 69
Sebastian AR 4 10 1.0 2661 2090 39
Benton AR 3 13 1.1 5058 1950 24
Crittenden AR 9 27 1.0 1565 3190 15
Hot Spring AR 6 46 0.8 1684 5020 9

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 845.4 seconds to compute.
2020-08-23 06:51:34

version history

Today is 2020-08-23.
95 days ago: Multiple states.
87 days ago: \(R_e\) computation.
84 days ago: created color coding for \(R_e\) plots.
79 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
79 days ago: “persistence” time evolution.
72 days ago: “In control” mapping.
72 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
64 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
59 days ago: Added Per Capita US Map.
57 days ago: Deprecated national map.
53 days ago: added state “Hot 10” analysis.
48 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
46 days ago: added per capita disease and mortaility to state-level analysis.
34 days ago: changed to county boundaries on national map for per capita disease.
29 days ago: corrected factor of two error in death trend data.
25 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.
20 days ago: added county level “baseline control” and \(R_e\) maps.
16 days ago: fixed normalization error on total disease stats plot.
9 days ago: Corrected some text matching in generating county level plots of \(R_e\).
3 days ago: adapter knot spacing for spline.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.